Computational protocol: Polymorphisms in genes of interleukin 12 and its receptors and their association with protection against severe malarial anaemia in children in western Kenya

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Protocol publication

[…] For tagging SNP (tagSNP) selection, LDSelect software at default settings (r2 > 0.65) was used to select tagSNPs from all common variation within four IL12 related genes from a public database of SeattleSNPs []. LDSelect uses an efficient selection algorithm to select tagging SNPs based on linkage disequilibrium (LD) statistic r2 and does not require direct haplotype inference []. At each round of selection, the binning algorithm identifies single SNP, which exceeds threshold r2 with the maximum number of other SNPs, and sets this group of SNPs as a bin. Then each SNP within the bin is analysed to determine whether it exceeds the threshold r2 with all other SNPs in the bin. All SNPs in a bin that meet this criterion are designated as tagSNPs. Only one tagSNP needs to be typed per bin. Binning criteria for tagSNP selection used in this study were minor allele frequency (MAF) cut-off of 10% and an r2 threshold of 0.65. LD varies across different populations [,]; therefore, only genotype data for the African-American samples present in the database was used, as this population is most similar to the samples in this study from western Kenya. Fifty five tagSNPs, 7 in IL12 A, 10 in IL12B, 22 in IL12RB1, and 16 in IL12RB2 were selected. Many more SNPs were needed for IL12RB1 gene than for other genes tested due to its larger size and the genetic diversity. [...] Genotypes were individually examined via a detailed quality control process involving duplicate calling of genotypes, control samples, evaluation of missing calls, and Hardy--Weinberg Equilibrium (HWE) testing. The iPLEX genotype clusters were manually checked. Only SNPs with > 90% calling rate and MAF > 2% were including in the final analysis. Each SNP was analysed using univariate methods and then multivariate Poisson regression analysis to ascertain the association between genotypes and malaria-associated morbidity. Generalized estimating equations and an independent working correlation structure to adjust for correlation between multiple visits from the same individual were used. In the multivariate Poisson regression, SNPs were independently evaluated as covariates using one of four genetic models: dominant ((AA + Aa) vs aa), recessive (AA vs (Aa + aa)), additive (aa vs Aa vs AA, which treat the marker as a continuous variable), and heterozygous advantage (Aa vs (AA + aa)) with three levels based on the most common allele frequency. Here, "A" refers the common allele. The results of the Poisson regression were reported as rate ratios (or risk ratios, RR). Multivariate models were adjusted for confounders, sickle cell type and treatment with anti-malarial drugs since these two factors were significantly associated with all four clinical phenotypes in univariate analysis (Table ). Data was analysed using SAS software package (version 9.1). Bonferroni adjusted critical alpha level of 0.007 for IL12A, 0.006 for IL12B, 0.005 for IL12RB1, and 0.004 for IL12RB2 were used. Haploview was used to obtain values for LD and perform haplotype block analysis of SNPs in the same gene [,]. D prime values (D') were calculated as statistical values for pairwise LD analysis between SNPs. […]

Pipeline specifications

Software tools SNPinfo, ldSelect, Haploview
Application GWAS
Organisms Homo sapiens, Mus musculus
Diseases Anemia, Malaria